Construction schedule risk analysis – a hybrid machine learning approach

The UK commissions about £100 billion in infrastructure construction works every year. More than 50% of them finish later than planned, causing damage to the interests of stakeholders. The estimation of time-risk on construction projects is currently done subjectively, largely by experience despite there are many existing techniques available to analyse risk on the construction schedules. Unlike conventional methods that tend to depend on the accurate estimation of risk boundaries for each task, this research aims to proposes a hybrid method to assist planners in undertaking risk analysis using baseline schedules with improved accuracy. The proposed method is endowed with machine intelligence and is trained using a database of 293,263 tasks from a diverse sample of 302 completed infrastructure construction projects in the UK. It combines a Gaussian Mixture Modelling-based Empirical Bayesian Network and a Support Vector Machine followed by performing a Monte Carlo risk simulation. The former is used to investigate the uncertainty, correlated risk factors, and predict task duration deviations while the latter is used to return a time-risk simulated prediction. This study randomly selected 10 projects as case studies followed by comparing their results of the proposed hybrid method with Monte Carlo Simulation. Results indicated 54.4% more accurate prediction on project delays.

Design of Testability Structures With Security For Machine Intelligence Based Cryptosystem

During testing utmost all appropriate and suitable strategy needs to be established for consistent fault coverage, improved controllability and observability. The scan chains used in BIST allows some fine control over data propagations that is used as a backdoor to break the security over cryptographic cores. To alleviate these scan-based side-channel attacks, implementing a more inclusive security strategy is required to confuse the attacker and to ensure the key management process which is always a difficult task to task in cryptographic research. In this work for testing AES core Design-for-Testability (DfT) is considered with some random response compaction, bit masking during the scan process. In the proposed scan architecture, scan-based attack does not allow finding out actual computations which are related to the cipher transformations and key sequence. And observing the data through the scan structure is secured. The experimental results validate the potential metrics of the proposed scan model in terms of robustness to the scan attack and penalty gap that exists due to the inclusion of scan designs in AES core. Also investigate the selection of appropriate location points to implement the bit level modification to avoid attack for retrieving a key.

Supervised Classification Model Using Google Earth Engine Development Environment for Wasit Governorate

As a result of the advancements that have occurred in the technical field of geomatics, particularly after the development of developmental programming environments, they have become the most important machine for conducting image analyses of satellite data, creating and modifying spatial analysis tools, and performing large data analyses at a fast rate without the need for high-end specifications on the personal computer. This study has several objectives, including the definition and popularization of the use of the power of Google Earth Engine (GEE) in the speed of conducting spatial analyzes, which cite by conducting a classification at the level of a governorate and obtaining results with speed and relatively good quality. By using the Google Earth Engine (GEE) platform and through Javascript programming language, a classification of the land cover of Wasit Governorate, Iraq was created under the supervision of a satellite image (Landsat 8) by creating a training sample, Google Maps’ High Resolution basemap imagery was used to create this map to identify classes of landcover (water, bare soil, vegetation, and urban). Each source pixel is assigned to one of the previously mentioned classes. Then to create a land cover map of the region using the Statistical Machine Intelligence and Learning Engine (SMILE) classifier from the JAVA library, which is used by Google Earth Engine (GEE) to implement these algorithms. The result is an array of pixels (raster data). The pixel value represents the class that was previously determined by the samples.

Trans-AI: How to Build True AI or Real Machine Intelligence and Learning

We are at the edge of colossal changes. This is a critical moment of historical choice and opportunity. It could be the best 5 years ahead of us that we have ever had in human history or one of the worst, because we have all the power, technology and knowledge to create the most fundamental general-purpose technology (GPT), which could completely upend the whole human history. The most im-portant GPTs were fire, the wheel, language, writing, the printing press, the steam engine, electric power, information and telecommunications technology, all to be topped by real artificial intelligence technology. Our study refers to Why and How the Real Machine Intelligence or True AI or Real Su-perintelligence (RSI) could be designed and developed, deployed and distributed in the next 5 years. The whole idea of RSI took about three decades in three phases. The first conceptual model of Trans-AI was published in 1989. It covered all possible physical phenomena, effects and processes. The more extended model of Real AI was developed in 1999. A complete theory of superintelligence, with its reality model, global knowledge base, NL programing language, and master algorithm, was presented in 2008. The RSI project has been finally completed in 2020, with some key findings and discoveries being published on the EU AI Alliance/Futurium site in 20+ articles. The RSI features a unifying World Metamodel (Global Ontology), with a General Intelligence Framework (Master Algo-rithm), Standard Data Type Hierarchy, NL Programming Language, to effectively interact with the world by intelligent processing of its data, from the web data to the real-world data. The basic results with technical specifications, classifications, formulas, algorithms, designs and patterns, were kept as a trade secret and documented as the Corporate Confidential Report: How to Engineer Man-Machine Superintelligence 2025. As a member of EU AI Alliance, the author has proposed the Man-Machine RSI Platform as a key part of Transnational EU-Russia Project. To shape a smart and sustainable fu-ture, the world should invest into the RSI Science and Technology, for the Trans-AI paradigm is the way to an inclusive, instrumented, interconnected and intelligent world.

Industry 5.0: concept, formation and development

At present in Europe and Russia there are active discussions of the next mega stage of human social and economic development. It concerns the Industry 5.0 concept. In 2020–2021 members of scientific research and technological organizations discussed the aspects of Industry 5.0 at a number of well-known European and Russian forums. Major attention was paid to technologies maintaining Industry 5.0. As a result the world scientific community reached a consensus on two fronts: firstly, on integration of opportunities of the existing technologies of Industry 4.0 with the human-oriented approach of Industry 5.0 which will give way to harmonious interaction of human intelligence with cognitive calculations; and, secondly, on integration of human and machine intelligence to create collective intelligence which will make it possible to avoid technological singularity in future as well as provide for simultaneous humans’ evolution and technologies’ development. These will become the basis for creating a fundamentally new social, economic and cultural strategy of the society’s development for the coming decades. The strategy is based on applying collective intelligence and meta-system technologies in all spheres of life. For the first time in the world scientific practice the authors introduce the concept of Industry 5.0 as a cyber social system which allows the alliance of human and artificial intelligence aimed at creating collective super intelligence and becomes a source of harmonious technological development of human civilization. The authors present a neuro-ecosystem model of Industry 5.0 concept which will make it possible to set the task of implementing the systems of global meta-system strategized development of cognitive production and industry. Such systems are established as part of organization of cognitive production and new types of social and economic, cyber-social and industrial ecosystems by means of implementing collective intelligence and neuro-digital meta-technologies. This is the first stage of a new evolution process of developing Industry 4.0 concept, transition to Industry 5.0 and to meta-system transformations economics.

Essential Features in a Theory of Context for Enabling Artificial General Intelligence

Despite recent Artificial Intelligence (AI) advances in narrow task areas such as face recognition and natural language processing, the emergence of general machine intelligence continues to be elusive. Such an AI must overcome several challenges, one of which is the ability to be aware of, and appropriately handle, context. In this article, we argue that context needs to be rigorously treated as a first-class citizen in AI research and discourse for achieving true general machine intelligence. Unfortunately, context is only loosely defined, if at all, within AI research. This article aims to synthesize the myriad pragmatic ways in which context has been used, or implicitly assumed, as a core concept in multiple AI sub-areas, such as representation learning and commonsense reasoning. While not all definitions are equivalent, we systematically identify a set of seven features associated with context in these sub-areas. We argue that such features are necessary for a sufficiently rich theory of context, as applicable to practical domains and applications in AI.

Save Face or Save Life: Physicians’ Dilemma in Using Clinical Decision Support Systems

Humans think both rationally and heuristically. So do physicians. Clinical decision support systems (CDSSs) provide advice to physicians that could save patients’ lives, but they could also make physicians feel face loss because of submission to machine intelligence, leading to a perplexing dilemma. Thinking rationally, physicians focus on fulfilling their professional duty to save patients and should follow advice from CDSS to improve care quality. Thinking heuristically, they focus on protecting their authoritative image to maintain face and are inclined to avoid embarrassment by resisting CDSS. Through a longitudinal survey and follow-up interviews with a group of Chinese physicians, we find that the dilemma does exist. Moreover, face loss has a stronger effect on CDSS resistance when physicians have high autonomy. When time pressure is high, perceived usefulness more strongly reduces, whereas face loss more strongly increases CDSS resistance, worsening the dilemma. As face is a universal social concern existing in both Eastern and Western cultures, this research generates insights regarding why physicians are slow in adopting information technology innovations.